Verification, Validation, and Predictive Capability: What’s What?

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Overview

Engineering organizations must increasingly rely on computational simulation for design and predicted performance, reliability, and safety of systems. Computational analysts, designers, decision makers, and project managers who rely on simulation should ensure that practical techniques and methods are in-place for assessing simulation credibility.

This webinar presents an introduction to the topics of verification, validation, and predictive capability. These topics are applicable to essentially all engineering and science applications, including fluid dynamics, heat transfer, solid mechanics, and structural dynamics. The mathematical models for the systems of interest are typically given by partial differential or integral equations representing initial value and boundary value problems. The computer codes that implement the mathematical models can be developed by commercial, corporate, government, or research organizations; but they should all be subjected to rigorous testing by way of verification procedures. The accuracy of the mathematical models coded into software are assessed by way of comparisons of results with experimental measurements; referred to as validation activities.

This webinar will sketch a framework for incorporating a wide range of error and uncertainty sources identified during the mathematical modeling, verification, and validation processes, with the goal of estimating the total predictive uncertainty of the simulation. This is referred to as predictive capability because typically no experimental measurements are available for the systems or subsystems at the application conditions of interest.

About the Presenters

Dr. William Oberkampf, Engineering Consultant, has 43 years of experience in research and development in fluid dynamics, heat transfer, flight dynamics, and solid mechanics. He spent his entire career in both computational and experimental areas. During the last 20 years, Dr. Oberkampf emphasized research and development in methodologies and procedures for verification, validation, and uncertainty quantification in computational simulations. He has written over 177 journal articles, book chapters, conference papers, and technical reports. He has taught 44 short courses in the field of verification, validation, and uncertainty quantification. Dr. Oberkampf received his B.S. in Aerospace Engineering in 1966 from the University of Notre Dame, his M.S. in Mechanical Engineering from the University of Texas at Austin in 1968, and his Ph.D. in 1970 in Aerospace Engineering from the University of Notre Dame. Dr. Oberkampf served on the faculty of the Mechanical Engineering Department at the University of Texas at Austin for nine years. After 29 years of service in both staff member and management positions at Sandia National Laboratories, he retired as a Distinguished Member of the Technical Staff. Since this time, he has been a consultant to the National Aeronautics and Space Administration, the U.S. Air Force, various Department of Energy laboratories, and corporations in the U.S. and Europe. He is a fellow of the American Institute of Aeronautics and Astronautics.

Professor Christopher Roy, Virginia Tech, holds a B.S. in Mechanical Engineering from Duke University, an M.S. in Aerospace Engineering from Texas A&M University, and a Ph.D. in Aerospace Engineering from North Carolina State University. From 1998 to 2003, he worked as a senior member of the technical staff in the Engineering Sciences Center at Sandia National Laboratories in Albuquerque, New Mexico. From 2003 to 2007, he was an Assistant Professor in the Aerospace Engineering Department at Auburn University. In 2007, Dr. Roy joined the Aerospace and Ocean Engineering Department at Virginia Tech and currently holds the rank of full professor. He has written over 120 journal articles, books, book chapters, conference papers, and technical reports in the areas of verification, validation, and uncertainty quantification. He has taught 32 short courses in the field of verification, validation, and uncertainty quantification.